Comment on “The Prevalence of Abnormal Glucose Metabolism and Its Correlates in the Zhuang Nationality, Nanning, Guangxi Province”

We read with great interest the study by Qin et al. who report important population-level data on abnormal glucose metabolism in the Zhuang ethnic group, using oral glucose tolerance testing (OGTT) and a broad biochemical panel in 1951 adults [1]. The authors report a high overall prevalence of abnormal glucose metabolism (37.52%), with type 2 diabetes mellitus (T2DM) and pre-diabetes (PDM) rates of 9.7% and 23.17%, respectively, and identify age, elevated body mass index (BMI), hypertension, dyslipidemia, and alcohol consumption as key associated factors. The authors are to be commended for applying standardized metabolic phenotyping, including fasting blood glucose (GLU), 2-h postprandial glucose (2hPG), and glycated hemoglobin (HbA1C), together with anthropometry and lipid profiling, namely total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C). This approach produces a clinically actionable portrait of metabolic risk in an understudied ethnic minority. Building on these strengths, several points merit respectful consideration to increase the study's translational value. Enhancing metabolic characterization beyond single-time OGTT results would refine risk stratification. Inclusion of fasting insulin to calculate the homeostatic model assessment for insulin resistance (HOMA-IR), or targeted measurement of adipokines, could help disentangle insulin-resistance driven dysglycemia from beta-cell failure [2, 3]. Given that the current analysis relies on glucose-based indices alone, incorporation of these markers would allow more precise phenotypic stratification of individuals classified under PDM and T2DM categories in the reported cohort. The lipid variables reported, namely TC, TG, LDL-C, and HDL-C, are biologically interrelated. Reporting model diagnostics, for example variance inflation factors, and exploring interaction terms such as age × BMI, would clarify whether reported independent associations are robust to collinearity and effect modification. This is particularly relevant in light of the multivariate models where both TC and HDL-C or LDL-C were retained as independent predictors, raising the possibility of overlapping explanatory variance within the same lipid pathway. Lifestyle exposures were categorized broadly. Deploying more granular dietary and physical-activity instruments or validated consumption metrics may reveal modifiable behaviors that are amenable to intervention and reduce exposure misclassification. For instance, the current classification of high-fat diet and fruit or vegetable intake into coarse ordinal groups may attenuate dose–response relationships that are clinically relevant for targeted prevention strategies [4]. There is opportunity to strengthen the link between epidemiologic findings and intervention design. Rather than only reporting relative measures such as odds ratios, providing absolute risks or population attributable fractions for dominant risk factors would better inform resource allocation and prioritization of prevention strategies at the community level [5]. In a setting where more than one-third of the population demonstrates abnormal glucose metabolism, translating relative associations into absolute burden metrics would substantially enhance policy interpretability [6]. Embedding a mixed-methods component, including brief qualitative interviews exploring cultural food practices, health literacy, and healthcare access, could identify culturally specific barriers and facilitators that quantitative data alone cannot capture [7]. For future work in this population, prospective follow-up to ascertain progression from pre-diabetes to type 2 diabetes mellitus, along with incorporation of pragmatic biomarkers, would provide greater insight into disease evolution, while evaluation of culturally tailored lifestyle interventions may facilitate translation into practice. Data sharing through de-identified, harmonized datasets with clear variable definitions would support external validation and cross-population analyses. Taken together, Qin et al. provide a valuable epidemiologic foundation, and integrating refined metabolic phenotyping, robust model diagnostics, and more granular exposure assessment would improve interpretability and strengthen the clinical and public health applicability of these findings. Shyam Sundar Sah: conceptualization, methodology, writing – original draft, writing – review and editing. Abhishek Kumbhalwar: validation, supervision, project administration, writing – original draft, writing – review and editing. Generative AI Use Statement: Generative AI tools, including Paperpal and ChatGPT 5, were utilized solely for language, grammar, and stylistic refinement. These tools had no role in the conceptualization, data analysis, interpretation of results, or substantive content development of this manuscript. All intellectual contributions, data analysis, and scientific interpretations remain the sole work of the authors. The final content was critically reviewed and edited to ensure accuracy and originality. The authors take full responsibility for the accuracy, originality, and integrity of the work presented. The authors have nothing to report. The authors have nothing to report. The authors have nothing to report. The authors declare no conflicts of interest. The authors have nothing to report.